Genome-wide association studies (GWAS) in African-ancestry populations have yet to provide a large payoff in identifying robust novel associations to infectious diseases such as malaria and tuberculosis, even though resistance to these diseases is known to include a substantial heritable component. Genetic variants that affect the risk of infectious disease are often under natural selection, leading to strong signals of unusual population differentiation between closely related populations that experienced different selective pressures. We and others have previously applied this approach to detect signals of selection at risk variants for malaria and other infectious diseases, and have shown that this approach improves power to identify disease associations. This approach is optimally powered when genome-wide genetic differences between populations are small, so that differences at the risk variants of interest lie outside the genome-wide distribution. However, when analyzing closely related populations, very large sample sizes are needed to minimize sampling noise. Previous work in this area has been limited by the minimal availability of genotype data from closely related African-ancestry populations in large sample size. Now, GWAS data for malaria, tuberculosis and other traits in multiple closely related African-ancestry populations with thousands of samples provides an appealing opportunity to proceed with this research. Here, we will analyze West African and African-American data sets to identify signals of natural selection via unusual population differentiation, while addressing the complication of European admixture in African-American samples. Furthermore, we will combine these signals of selection with those produced by independent approaches, to increase power to identify and localize selected variants. Our findings will be of high interest to investigators aiming to identify the genetic basis for malaria, tuberculosis, and other infectious diseases.